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  {
   "cell_type": "raw",
   "id": "67db2992",
   "metadata": {},
   "source": [
    "---\n",
    "sidebar_label: __ModuleName__\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9597802c",
   "metadata": {},
   "source": [
    "# __ModuleName__LLM\n",
    "\n",
    "This example goes over how to use LangChain to interact with `__ModuleName__` models.\n",
    "\n",
    "## Installation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "59c710c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# install package\n",
    "!pip install -U __package_name__"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ee90032",
   "metadata": {},
   "source": [
    "## Environment Setup\n",
    "\n",
    "Make sure to set the following environment variables:\n",
    "\n",
    "- TODO: fill out relevant environment variables or secrets\n",
    "\n",
    "## Usage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "035dea0f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from langchain_core.prompts import PromptTemplate\n",
    "from __module_name__.llms import __ModuleName__LLM\n",
    "\n",
    "template = \"\"\"Question: {question}\n",
    "\n",
    "Answer: Let's think step by step.\"\"\"\n",
    "\n",
    "prompt = PromptTemplate.from_string(template)\n",
    "\n",
    "model = __ModuleName__LLM()\n",
    "\n",
    "chain = prompt | model\n",
    "\n",
    "chain.invoke({\"question\": \"What is LangChain?\"})"
   ]
  }
 ],
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